18 citations · 36 across the 12 of their papers we have counts for
11 papers · 1 filter
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz +1
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The p…
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi +2
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects f…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Tree Variational Autoencoders
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser +1
We propose Tree Variational Autoencoder (TreeVAE), a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. Tr…
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Moritz Vandenhirtz, Laura Manduchi, Ričards Marcinkevičs +1
Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of th…
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
Alain Ryser, Laura Manduchi, Fabian Laumer +3
We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a…
Learning Group Importance using the Differentiable Hypergeometric Distribution
Thomas M. Sutter, Laura Manduchi, Alain Ryser +1
Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes…
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Laura Manduchi, Kieran Chin-Cheong, Holger Michel +2
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Fo…
A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi +9
In this work, we study the problem of clustering survival data a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to clus…
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Laura Manduchi, Matthias Hüser, Julia Vogt +2
Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation le…